Computerized systems and methods for address correction

ABSTRACT

A computer-implemented system for correcting address information. The system may include a memory storing instructions and at least one processor configured to execute the instructions to perform operations. The operations may include requesting an address for normalization from at least one of a current address or a residential history of a user; receiving, from a user device, a user input including requested address information responsive to the request for normalization; searching, based on the user input, a cache to determine whether a refined version of the requested address is available; returning, based on a determination that a refined version of the requested address exists in the cache, a refined address as the normalized address to the user; and beginning to transport a package to the user at the normalized address, by providing instructions to a mobile device associated with a delivery worker, to transport the package to the normalized address.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims the benefit of priorityto U.S. application Ser. No. 16/511,610, filed Jul. 15, 2019 (nowallowed), the contents of which are incorporated herein by reference intheir entirety.

TECHNICAL FIELD

The present disclosure generally relates to computerized systems andmethods for address correction. In particular, embodiments of thepresent disclosure relate to inventive and unconventional systems formachine learning and automatic correction and display of correctedaddresses based on user input to optimize delivery outcomes.

BACKGROUND

Computerized systems enable users to enter their addresses for receivinga shipment. Users may enter their address in many forms (street name,apartment name, apartment number, city, etc.). However, sometimes usersinput incorrect information. For example, sometimes a user may misspella street name or include an incorrect numeral as part of a streetaddress. Other times, a user will input correct information, but thatinformation will be non-standard and thus difficult to properly map orlocate for sending to other systems (e.g., to a delivery worker'sdevice). For example, a user may input a postal code but may onlyinclude the first digits of the postal code (and not the remainingdigits of a full postal code number), or a user may enter anabbreviation for a street name that is not a standard abbreviation. As aresult, this address information may be difficult to map or locate forsending to other systems.

Current address correction systems may provide for correction ofmisspellings and may standardize non-standard address input. Currentaddress correction systems may also provide a user interface for entryand storage of multiple addresses and may allow for display of acorrected address for the user. For example, a user may enter a shippingaddress for an online purchase of a product, and current correctedaddress systems may fix or correct the shipping address for display tothe user before the user approves and subsequently completes purchase ofthe online product. In this instance, a user may be able to review andapprove the corrected address before proceeding to the next step of theonline purchase. However, current addresses correction systems arelimited in the user interfaces that may be displayed to a user and arelimited in the type of errors in input address information that they maybe able to detect and correct. Furthermore, current address correctionsystems are unable to analyze address information over time to determinepatterns for automatic correction of new address input.

Therefore, what is needed is a system that is capable of enablingreceipt of an address from a user (e.g., via a website form, via amobile device, or from a user database). Further, what is needed is asystem that will consider an address and determine whether the addressis abnormal in any way. This may include comparing addresses againstmaps, known location information (e.g., names, apartment names, etc.),and checking the spelling of addresses. Finally, what is needed areimproved methods and systems for automatic address correction to checkfor patterns to determine common mistakes that users make and to developpatterns for automatic correction of future addresses.

SUMMARY

One aspect of the present disclosure is directed to a computerizedsystem for address correction. The system may include a memory and aprocessor configured to execute instructions to perform operations. Theoperations may include requesting an address for normalization from atleast one of a current address or a residential history of a user;receiving, from a user device, a user input including requested addressinformation responsive to the request for normalization; searching,based on the user input, a cache to determine whether a refined versionof the requested address is available; returning, based on adetermination that a refined version of the requested address exists inthe cache, a refined address as the normalized address to the user;beginning to transport a package to the user at the normalized address,by providing instructions to a mobile device associated with a deliveryworker, to transport the package to the normalized address; comparing,while the package is in transit, the normalized address location to acurrent location of the delivery worker; if it is determined that thenormalized address location does not match the current location of thedelivery worker: transmitting a warning to the user device; correctingthe normalized address, based on a machine learning process; storing,based on the correction, the corrected normalized address in a database;and providing instructions to deliver the package to the user at thedelivery location based on the normalized address or the correctednormalized address.

Another aspect of the present disclosure is directed to a computerizedmethod for address correction. The method may perform operationsincluding requesting an address for normalization from at least one of acurrent address or a residential history of a user; receiving, from auser device, a user input including requested address informationresponsive to the request for normalization; searching, based on theuser input, a cache to determine whether a refined version of therequested address is available; returning, based on a determination thata refined version of the requested address exists in the cache, arefined address as the normalized address to the user; beginning totransport a package to the user at the normalized address, by providinginstructions to a mobile device associated with a delivery worker, totransport the package to the normalized address; comparing, while thepackage is in transit, the normalized address location to a currentlocation of the delivery worker; if it is determined that the normalizedaddress location does not match the current location of the deliveryworker: transmitting a warning to the user device; correcting thenormalized address, based on a machine learning process; storing, basedon the correction, the corrected normalized address in a database; andproviding instructions to deliver the package to the user at thedelivery location based on the normalized address or the correctednormalized address.

Yet another aspect of the present disclosure is directed to anon-transitory computer readable medium comprising executableinstructions that when executed by at least one processing device causethe at least one processing device to correct address information andperform operations comprising requesting an address for normalizationfrom at least one of a current address or a residential history of auser; receiving, from a user device, a user input including requestedaddress information responsive to the request for normalization;searching, based on the user input, a cache to determine whether arefined version of the requested address is available; returning, basedon a determination that a refined version of the requested addressexists in the cache, a refined address as the normalized address to theuser; beginning to transport a package to the user at the normalizedaddress, by providing instructions to a mobile device associated with adelivery worker, to transport the package to the normalized address;comparing, while the package is in transit, the normalized addresslocation to a current location of the delivery worker; if it isdetermined that the normalized address location does not match thecurrent location of the delivery worker: transmitting a warning to theuser device; correcting the normalized address, based on a machinelearning process; storing, based on the correction, the correctednormalized address in a database; and providing instructions to deliverthe package to the user at the delivery location based on the normalizedaddress or the corrected normalized address.

Other systems, methods, and computer-readable media are also discussedherein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a schematic block diagram illustrating an exemplaryembodiment of a network comprising computerized systems forcommunications enabling shipping, transportation, and logisticsoperations, consistent with the disclosed embodiments.

FIG. 1B depicts a sample Search Result Page (SRP) that includes one ormore search results satisfying a search request along with interactiveuser interface elements, consistent with the disclosed embodiments.

FIG. 1C depicts a sample Single Display Page (SDP) that includes aproduct and information about the product along with interactive userinterface elements, consistent with the disclosed embodiments.

FIG. 1D depicts a sample Cart page that includes items in a virtualshopping cart along with interactive user interface elements, consistentwith the disclosed embodiments.

FIG. 1E depicts a sample Order page that includes items from the virtualshopping cart along with information regarding purchase and shipping,along with interactive user interface elements, consistent with thedisclosed embodiments.

FIG. 2 is a diagrammatic illustration of an exemplary fulfillment centerconfigured to utilize disclosed computerized systems, consistent withthe disclosed embodiments.

FIG. 3 is a flow chart illustrating an exemplary process fornormalization, consistent with the disclosed embodiments.

FIG. 4 is a flow chart illustrating an exemplary process for machinelearning, consistent with the disclosed embodiments.

FIG. 5 is a diagrammatic illustration of an exemplary learning curve,consistent with the disclosed embodiments.

FIG. 6 is a flow chart illustrating an exemplary process for collectionof address coordination information, consistent with the disclosedembodiments.

FIG. 7 is a flow chart illustrating an exemplary process for addressmatching, consistent with the disclosed embodiments.

FIG. 8 is a flow chart illustrating an exemplary process for correctingaddress information, consistent with the disclosed embodiments.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts.While several illustrative embodiments are described herein,modifications, adaptations and other implementations are possible. Forexample, substitutions, additions, or modifications may be made to thecomponents and steps illustrated in the drawings, and the illustrativemethods described herein may be modified by substituting, reordering,removing, or adding steps to the disclosed methods. Accordingly, thefollowing detailed description is not limited to the disclosedembodiments and examples. Instead, the proper scope of the invention isdefined by the appended claims.

Embodiments of the present disclosure are directed to computerizedsystems and methods configured for address correction.

Referring to FIG. 1A, a schematic block diagram 100 illustrating anexemplary embodiment of a system comprising computerized systems forcommunications enabling shipping, transportation, and logisticsoperations is shown. As illustrated in FIG. 1A, system 100 may include avariety of systems, each of which may be connected to one another viaone or more networks. The systems may also be connected to one anothervia a direct connection, for example, using a cable. The depictedsystems include a shipment authority technology (SAT) system 101, anexternal front end system 103, an internal front end system 105, atransportation system 107, mobile devices 107A, 107B, and 107C, sellerportal 109, shipment and order tracking (SOT) system 111, fulfillmentoptimization (FO) system 113, fulfillment messaging gateway (FMG) 115,supply chain management (SCM) system 117, workforce management system119, mobile devices 119A, 119B, and 119C (depicted as being inside offulfillment center (FC) 200), 3^(rd) party fulfillment systems 121A,121B, and 121C, fulfillment center authorization system (FC Auth) 123,and labor management system (LMS) 125.

SAT system 101, in some embodiments, may be implemented as a computersystem that monitors order status and delivery status. For example, SATsystem 101 may determine whether an order is past its Promised DeliveryDate (PDD) and may take appropriate action, including initiating a neworder, reshipping the items in the non-delivered order, canceling thenon-delivered order, initiating contact with the ordering customer, orthe like. SAT system 101 may also monitor other data, including output(such as a number of packages shipped during a particular time period)and input (such as the number of empty cardboard boxes received for usein shipping). SAT system 101 may also act as a gateway between differentdevices in system 100, enabling communication (e.g., usingstore-and-forward or other techniques) between devices such as externalfront end system 103 and FO system 113.

External front end system 103, in some embodiments, may be implementedas a computer system that enables external users to interact with one ormore systems in system 100. For example, in embodiments where system 100enables the presentation of systems to enable users to place an orderfor an item, external front end system 103 may be implemented as a webserver that receives search requests, presents item pages, and solicitspayment information. For example, external front end system 103 may beimplemented as a computer or computers running software such as theApache HTTP Server, Microsoft Internet Information Services (IIS),NGINX, or the like. In other embodiments, external front end system 103may run custom web server software designed to receive and processrequests from external devices (e.g., mobile device 102A or computer102B), acquire information from databases and other data stores based onthose requests, and provide responses to the received requests based onacquired information.

In some embodiments, external front end system 103 may include one ormore of a web caching system, a database, a search system, or a paymentsystem. In one aspect, external front end system 103 may comprise one ormore of these systems, while in another aspect, external front endsystem 103 may comprise interfaces (e.g., server-to-server,database-to-database, or other network connections) connected to one ormore of these systems.

An illustrative set of steps, illustrated by FIGS. 1B, 1C, 1D, and 1E,will help to describe some operations of external front end system 103.External front end system 103 may receive information from systems ordevices in system 100 for presentation and/or display. For example,external front end system 103 may host or provide one or more web pages,including a Search Result Page (SRP) (e.g., FIG. 1B), a Single DetailPage (SDP) (e.g., FIG. 1C), a Cart page (e.g., FIG. 1D), or an Orderpage (e.g., FIG. 1E). A user device (e.g., using mobile device 102A orcomputer 102B) may navigate to external front end system 103 and requesta search by entering information into a search box. External front endsystem 103 may request information from one or more systems in system100. For example, external front end system 103 may request informationfrom FO System 113 that satisfies the search request. External front endsystem 103 may also request and receive (from FO System 113) a PromisedDelivery Date or “PDD” for each product included in the search results.The PDD, in some embodiments, may represent an estimate of when apackage containing the product will arrive at the user's desiredlocation or a date by which the product is promised to be delivered atthe user's desired location if ordered within a particular period oftime, for example, by the end of the day (11:59 PM). (PDD is discussedfurther below with respect to FO System 113.)

External front end system 103 may prepare an SRP (e.g., FIG. 1B) basedon the information. The SRP may include information that satisfies thesearch request. For example, this may include pictures of products thatsatisfy the search request. The SRP may also include respective pricesfor each product, or information relating to enhanced delivery optionsfor each product, PDD, weight, size, offers, discounts, or the like.External front end system 103 may send the SRP to the requesting userdevice (e.g., via a network).

A user device may then select a product from the SRP, e.g., by clickingor tapping a user interface, or using another input device, to select aproduct represented on the SRP. The user device may formulate a requestfor information on the selected product and send it to external frontend system 103. In response, external front end system 103 may requestinformation related to the selected product. For example, theinformation may include additional information beyond that presented fora product on the respective SRP. This could include, for example, shelflife, country of origin, weight, size, number of items in package,handling instructions, or other information about the product. Theinformation could also include recommendations for similar products(based on, for example, big data and/or machine learning analysis ofcustomers who bought this product and at least one other product),answers to frequently asked questions, reviews from customers,manufacturer information, pictures, or the like.

External front end system 103 may prepare an SDP (Single Detail Page)(e.g., FIG. 1C) based on the received product information. The SDP mayalso include other interactive elements such as a “Buy Now” button, a“Add to Cart” button, a quantity field, a picture of the item, or thelike. The SDP may further include a list of sellers that offer theproduct. The list may be ordered based on the price each seller offerssuch that the seller that offers to sell the product at the lowest pricemay be listed at the top. The list may also be ordered based on theseller ranking such that the highest ranked seller may be listed at thetop. The seller ranking may be formulated based on multiple factors,including, for example, the seller's past track record of meeting apromised PDD. External front end system 103 may deliver the SDP to therequesting user device (e.g., via a network).

The requesting user device may receive the SDP which lists the productinformation. Upon receiving the SDP, the user device may then interactwith the SDP. For example, a user of the requesting user device mayclick or otherwise interact with a “Place in Cart” button on the SDP.This adds the product to a shopping cart associated with the user. Theuser device may transmit this request to add the product to the shoppingcart to external front end system 103.

External front end system 103 may generate a Cart page (e.g., FIG. 1D).The Cart page, in some embodiments, lists the products that the user hasadded to a virtual “shopping cart.” A user device may request the Cartpage by clicking on or otherwise interacting with an icon on the SRP,SDP, or other pages. The Cart page may, in some embodiments, list allproducts that the user has added to the shopping cart, as well asinformation about the products in the cart such as a quantity of eachproduct, a price for each product per item, a price for each productbased on an associated quantity, information regarding PDD, a deliverymethod, a shipping cost, user interface elements for modifying theproducts in the shopping cart (e.g., deletion or modification of aquantity), options for ordering other product or setting up periodicdelivery of products, options for setting up interest payments, userinterface elements for proceeding to purchase, or the like. A user at auser device may click on or otherwise interact with a user interfaceelement (e.g., a button that reads “Buy Now”) to initiate the purchaseof the product in the shopping cart. Upon doing so, the user device maytransmit this request to initiate the purchase to external front endsystem 103.

External front end system 103 may generate an Order page (e.g., FIG. 1E)in response to receiving the request to initiate a purchase. The Orderpage, in some embodiments, re-lists the items from the shopping cart andrequests input of payment and shipping information. For example, theOrder page may include a section requesting information about thepurchaser of the items in the shopping cart (e.g., name, address, e-mailaddress, phone number), information about the recipient (e.g., name,address, phone number, delivery information), shipping information(e.g., speed/method of delivery and/or pickup), payment information(e.g., credit card, bank transfer, check, stored credit), user interfaceelements to request a cash receipt (e.g., for tax purposes), or thelike. External front end system 103 may send the Order page to the userdevice.

The user device may enter information on the Order page and click orotherwise interact with a user interface element that sends theinformation to external front end system 103. From there, external frontend system 103 may send the information to different systems in system100 to enable the creation and processing of a new order with theproducts in the shopping cart.

In some embodiments, external front end system 103 may be furtherconfigured to enable sellers to transmit and receive informationrelating to orders.

Internal front end system 105, in some embodiments, may be implementedas a computer system that enables internal users (e.g., employees of anorganization that owns, operates, or leases system 100) to interact withone or more systems in system 100. For example, in embodiments wherenetwork 101 enables the presentation of systems to enable users to placean order for an item, internal front end system 105 may be implementedas a web server that enables internal users to view diagnostic andstatistical information about orders, modify item information, or reviewstatistics relating to orders. For example, internal front end system105 may be implemented as a computer or computers running software suchas the Apache HTTP Server, Microsoft Internet Information Services(IIS), NGINX, or the like. In other embodiments, internal front endsystem 105 may run custom web server software designed to receive andprocess requests from systems or devices depicted in system 100 (as wellas other devices not depicted), acquire information from databases andother data stores based on those requests, and provide responses to thereceived requests based on acquired information.

In some embodiments, internal front end system 105 may include one ormore of a web caching system, a database, a search system, a paymentsystem, an analytics system, an order monitoring system, or the like. Inone aspect, internal front end system 105 may comprise one or more ofthese systems, while in another aspect, internal front end system 105may comprise interfaces (e.g., server-to-server, database-to-database,or other network connections) connected to one or more of these systems.

Transportation system 107, in some embodiments, may be implemented as acomputer system that enables communication between systems or devices insystem 100 and mobile devices 107A-107C. Transportation system 107, insome embodiments, may receive information from one or more mobiledevices 107A-107C (e.g., mobile phones, smart phones, PDAs, or thelike). For example, in some embodiments, mobile devices 107A-107C maycomprise devices operated by delivery workers. The delivery workers, whomay be permanent, temporary, or shift employees, may utilize mobiledevices 107A-107C to effect delivery of packages containing the productsordered by users. For example, to deliver a package, the delivery workermay receive a notification on a mobile device indicating which packageto deliver and where to deliver it. Upon arriving at the deliverylocation, the delivery worker may locate the package (e.g., in the backof a truck or in a crate of packages), scan or otherwise capture dataassociated with an identifier on the package (e.g., a barcode, an image,a text string, an RFID tag, or the like) using the mobile device, anddeliver the package (e.g., by leaving it at a front door, leaving itwith a security guard, handing it to the recipient, or the like). Insome embodiments, the delivery worker may capture photo(s) of thepackage and/or may obtain a signature using the mobile device. Themobile device may send information to transportation system 107including information about the delivery, including, for example, time,date, GPS location, photo(s), an identifier associated with the deliveryworker, an identifier associated with the mobile device, or the like.Transportation system 107 may store this information in a database (notpictured) for access by other systems in system 100. Transportationsystem 107 may, in some embodiments, use this information to prepare andsend tracking data to other systems indicating the location of aparticular package.

In some embodiments, certain users may use one kind of mobile device(e.g., permanent workers may use a specialized PDA with custom hardwaresuch as a barcode scanner, stylus, and other devices) while other usersmay use other kinds of mobile devices (e.g., temporary or shift workersmay utilize off-the-shelf mobile phones and/or smartphones).

In some embodiments, transportation system 107 may associate a user witheach device. For example, transportation system 107 may store anassociation between a user (represented by, e.g., a user identifier, anemployee identifier, or a phone number) and a mobile device (representedby, e.g., an International Mobile Equipment Identity (IMEI), anInternational Mobile Subscription Identifier (IMSI), a phone number, aUniversal Unique Identifier (UUID), or a Globally Unique Identifier(GUID)). Transportation system 107 may use this association inconjunction with data received on deliveries to analyze data stored inthe database in order to determine, among other things, a location ofthe worker, an efficiency of the worker, or a speed of the worker.

Seller portal 109, in some embodiments, may be implemented as a computersystem that enables sellers or other external entities to electronicallycommunicate with one or more systems in system 100. For example, aseller may utilize a computer system (not pictured) to upload or provideproduct information, order information, contact information, or thelike, for products that the seller wishes to sell through system 100using seller portal 109.

Shipment and order tracking system 111, in some embodiments, may beimplemented as a computer system that receives, stores, and forwardsinformation regarding the location of packages containing productsordered by customers (e.g., by a user using devices 102A-102B). In someembodiments, shipment and order tracking system 111 may request or storeinformation from web servers (not pictured) operated by shippingcompanies that deliver packages containing products ordered bycustomers.

In some embodiments, shipment and order tracking system 111 may requestand store information from systems depicted in system 100. For example,shipment and order tracking system 111 may request information fromtransportation system 107. As discussed above, transportation system 107may receive information from one or more mobile devices 107A-107C (e.g.,mobile phones, smart phones, PDAs, or the like) that are associated withone or more of a user (e.g., a delivery worker) or a vehicle (e.g., adelivery truck). In some embodiments, shipment and order tracking system111 may also request information from workforce management system (WMS)119 to determine the location of individual products inside of afulfillment center (e.g., fulfillment center 200). Shipment and ordertracking system 111 may request data from one or more of transportationsystem 107 or WMS 119, process it, and present it to a device (e.g.,user devices 102A and 102B) upon request.

Fulfillment optimization (FO) system 113, in some embodiments, may beimplemented as a computer system that stores information for customerorders from other systems (e.g., external front end system 103 and/orshipment and order tracking system 111). FO system 113 may also storeinformation describing where particular items are held or stored. Forexample, certain items may be stored only in one fulfillment center,while certain other items may be stored in multiple fulfillment centers.In still other embodiments, certain fulfilment centers may be designedto store only a particular set of items (e.g., fresh produce or frozenproducts). FO system 113 stores this information as well as associatedinformation (e.g., quantity, size, date of receipt, expiration date,etc.).

FO system 113 may also calculate a corresponding PDD (promised deliverydate) for each product. The PDD, in some embodiments, may be based onone or more factors. For example, FO system 113 may calculate a PDD fora product based on a past demand for a product (e.g., how many timesthat product was ordered during a period of time), an expected demandfor a product (e.g., how many customers are forecast to order theproduct during an upcoming period of time), a network-wide past demandindicating how many products were ordered during a period of time, anetwork-wide expected demand indicating how many products are expectedto be ordered during an upcoming period of time, one or more counts ofthe product stored in each fulfillment center 200, which fulfillmentcenter stores each product, expected or current orders for that product,or the like.

In some embodiments, FO system 113 may determine a PDD for each producton a periodic basis (e.g., hourly) and store it in a database forretrieval or sending to other systems (e.g., external front end system103, SAT system 101, shipment and order tracking system 111). In otherembodiments, FO system 113 may receive electronic requests from one ormore systems (e.g., external front end system 103, SAT system 101,shipment and order tracking system 111) and calculate the PDD on demand.

Fulfilment messaging gateway (FMG) 115, in some embodiments, may beimplemented as a computer system that receives a request or response inone format or protocol from one or more systems in system 100, such asFO system 113, converts it to another format or protocol, and forward itin the converted format or protocol to other systems, such as WMS 119 or3^(rd) party fulfillment systems 121A, 121B, or 121C, and vice versa.

Supply chain management (SCM) system 117, in some embodiments, may beimplemented as a computer system that performs forecasting functions.For example, SCM system 117 may forecast a level of demand for aparticular product based on, for example, based on a past demand forproducts, an expected demand for a product, a network-wide past demand,a network-wide expected demand, a count products stored in eachfulfillment center 200, expected or current orders for each product, orthe like. In response to this forecasted level and the amount of eachproduct across all fulfillment centers, SCM system 117 may generate oneor more purchase orders to purchase and stock a sufficient quantity tosatisfy the forecasted demand for a particular product.

Workforce management system (WMS) 119, in some embodiments, may beimplemented as a computer system that monitors workflow. For example,WMS 119 may receive event data from individual devices (e.g., devices107A-107C or 119A-119C) indicating discrete events. For example, WMS 119may receive event data indicating the use of one of these devices toscan a package. As discussed below with respect to fulfillment center200 and FIG. 2, during the fulfillment process, a package identifier(e.g., a barcode or RFID tag data) may be scanned or read by machines atparticular stages (e.g., automated or handheld barcode scanners, RFIDreaders, high-speed cameras, devices such as tablet 119A, mobiledevice/PDA 1198, computer 119C, or the like). WMS 119 may store eachevent indicating a scan or a read of a package identifier in acorresponding database (not pictured) along with the package identifier,a time, date, location, user identifier, or other information, and mayprovide this information to other systems (e.g., shipment and ordertracking system 111).

WMS 119, in some embodiments, may store information associating one ormore devices (e.g., devices 107A-107C or 119A-119C) with one or moreusers associated with system 100. For example, in some situations, auser (such as a part- or full-time employee) may be associated with amobile device in that the user owns the mobile device (e.g., the mobiledevice is a smartphone). In other situations, a user may be associatedwith a mobile device in that the user is temporarily in custody of themobile device (e.g., the user checked the mobile device out at the startof the day, will use it during the day, and will return it at the end ofthe day).

WMS 119, in some embodiments, may maintain a work log for each userassociated with system 100. For example, WMS 119 may store informationassociated with each employee, including any assigned processes (e.g.,unloading trucks, picking items from a pick zone, rebin wall work,packing items), a user identifier, a location (e.g., a floor or zone ina fulfillment center 200), a number of units moved through the system bythe employee (e.g., number of items picked, number of items packed), anidentifier associated with a device (e.g., devices 119A-119C), or thelike. In some embodiments, WMS 119 may receive check-in and check-outinformation from a timekeeping system, such as a timekeeping systemoperated on a device 119A-119C.

3^(rd) party fulfillment (3PL) systems 121A-121C, in some embodiments,represent computer systems associated with third-party providers oflogistics and products. For example, while some products are stored infulfillment center 200 (as discussed below with respect to FIG. 2),other products may be stored off-site, may be produced on demand, or maybe otherwise unavailable for storage in fulfillment center 200. 3PLsystems 121A-121C may be configured to receive orders from FO system 113(e.g., through FMG 115) and may provide products and/or services (e.g.,delivery or installation) to customers directly. In some embodiments,one or more of 3PL systems 121A-121C may be part of system 100, while inother embodiments, one or more of 3PL systems 121A-121C may be outsideof system 100 (e.g., owned or operated by a third-party provider).

Fulfillment Center Auth system (FC Auth) 123, in some embodiments, maybe implemented as a computer system with a variety of functions. Forexample, in some embodiments, FC Auth 123 may act as a single-sign on(SSO) service for one or more other systems in system 100. For example,FC Auth 123 may enable a user to log in via internal front end system105, determine that the user has similar privileges to access resourcesat shipment and order tracking system 111, and enable the user to accessthose privileges without requiring a second log in process. FC Auth 123,in other embodiments, may enable users (e.g., employees) to associatethemselves with a particular task. For example, some employees may nothave an electronic device (such as devices 119A-119C) and may insteadmove from task to task, and zone to zone, within a fulfillment center200, during the course of a day. FC Auth 123 may be configured to enablethose employees to indicate what task they are performing and what zonethey are in at different times of day.

Labor management system (LMS) 125, in some embodiments, may beimplemented as a computer system that stores attendance and overtimeinformation for employees (including full-time and part-time employees).For example, LMS 125 may receive information from FC Auth 123, WMA 119,devices 119A-119C, transportation system 107, and/or devices 107A-107C.

The particular configuration depicted in FIG. 1A is an example only. Forexample, while FIG. 1A depicts FC Auth system 123 connected to FO system113, not all embodiments require this particular configuration. Indeed,in some embodiments, the systems in system 100 may be connected to oneanother through one or more public or private networks, including theInternet, an Intranet, a WAN (Wide-Area Network), a MAN(Metropolitan-Area Network), a wireless network compliant with the IEEE802.11a/b/g/n Standards, a leased line, or the like. In someembodiments, one or more of the systems in system 100 may be implementedas one or more virtual servers implemented at a data center, serverfarm, or the like.

FIG. 2 depicts a fulfillment center 200. Fulfillment center 200 is anexample of a physical location that stores items for shipping tocustomers when ordered. Fulfillment center (FC) 200 may be divided intomultiple zones, each of which are depicted in FIG. 2. These “zones,” insome embodiments, may be thought of as virtual divisions betweendifferent stages of a process of receiving items, storing the items,retrieving the items, and shipping the items. So while the “zones” aredepicted in FIG. 2, other divisions of zones are possible, and the zonesin FIG. 2 may be omitted, duplicated, or modified in some embodiments.

Inbound zone 203 represents an area of FC 200 where items are receivedfrom sellers who wish to sell products using system 100 from FIG. 1A.For example, a seller may deliver items 202A and 202B using truck 201.Item 202A may represent a single item large enough to occupy its ownshipping pallet, while item 202B may represent a set of items that arestacked together on the same pallet to save space.

A worker will receive the items in inbound zone 203 and may optionallycheck the items for damage and correctness using a computer system (notpictured). For example, the worker may use a computer system to comparethe quantity of items 202A and 202B to an ordered quantity of items. Ifthe quantity does not match, that worker may refuse one or more of items202A or 202B. If the quantity does match, the worker may move thoseitems (using, e.g., a dolly, a handtruck, a forklift, or manually) tobuffer zone 205. Buffer zone 205 may be a temporary storage area foritems that are not currently needed in the picking zone, for example,because there is a high enough quantity of that item in the picking zoneto satisfy forecasted demand. In some embodiments, forklifts 206 operateto move items around buffer zone 205 and between inbound zone 203 anddrop zone 207. If there is a need for items 202A or 202B in the pickingzone (e.g., because of forecasted demand), a forklift may move items202A or 202B to drop zone 207.

Drop zone 207 may be an area of FC 200 that stores items before they aremoved to picking zone 209. A worker assigned to the picking task (a“picker”) may approach items 202A and 202B in the picking zone, scan abarcode for the picking zone, and scan barcodes associated with items202A and 202B using a mobile device (e.g., device 119B). The picker maythen take the item to picking zone 209 (e.g., by placing it on a cart orcarrying it).

Picking zone 209 may be an area of FC 200 where items 208 are stored onstorage units 210. In some embodiments, storage units 210 may compriseone or more of physical shelving, bookshelves, boxes, totes,refrigerators, freezers, cold stores, or the like. In some embodiments,picking zone 209 may be organized into multiple floors. In someembodiments, workers or machines may move items into picking zone 209 inmultiple ways, including, for example, a forklift, an elevator, aconveyor belt, a cart, a handtruck, a dolly, an automated robot ordevice, or manually. For example, a picker may place items 202A and 202Bon a handtruck or cart in drop zone 207 and walk items 202A and 202B topicking zone 209.

A picker may receive an instruction to place (or “stow”) the items inparticular spots in picking zone 209, such as a particular space on astorage unit 210. For example, a picker may scan item 202A using amobile device (e.g., device 119B). The device may indicate where thepicker should stow item 202A, for example, using a system that indicatean aisle, shelf, and location. The device may then prompt the picker toscan a barcode at that location before stowing item 202A in thatlocation. The device may send (e.g., via a wireless network) data to acomputer system such as WMS 119 in FIG. 1A indicating that item 202A hasbeen stowed at the location by the user using device 1198.

Once a user places an order, a picker may receive an instruction ondevice 1198 to retrieve one or more items 208 from storage unit 210. Thepicker may retrieve item 208, scan a barcode on item 208, and place iton transport mechanism 214. While transport mechanism 214 is representedas a slide, in some embodiments, transport mechanism may be implementedas one or more of a conveyor belt, an elevator, a cart, a forklift, ahandtruck, a dolly, a cart, or the like. Item 208 may then arrive atpacking zone 211.

Packing zone 211 may be an area of FC 200 where items are received frompicking zone 209 and packed into boxes or bags for eventual shipping tocustomers. In packing zone 211, a worker assigned to receiving items (a“rebin worker”) will receive item 208 from picking zone 209 anddetermine what order it corresponds to. For example, the rebin workermay use a device, such as computer 119C, to scan a barcode on item 208.Computer 119C may indicate visually which order item 208 is associatedwith. This may include, for example, a space or “cell” on a wall 216that corresponds to an order. Once the order is complete (e.g., becausethe cell contains all items for the order), the rebin worker mayindicate to a packing worker (or “packer”) that the order is complete.The packer may retrieve the items from the cell and place them in a boxor bag for shipping. The packer may then send the box or bag to a hubzone 213, e.g., via forklift, cart, dolly, handtruck, conveyor belt,manually, or otherwise.

Hub zone 213 may be an area of FC 200 that receives all boxes or bags(“packages”) from packing zone 211. Workers and/or machines in hub zone213 may retrieve package 218 and determine which portion of a deliveryarea each package is intended to go to, and route the package to anappropriate camp zone 215. For example, if the delivery area has twosmaller sub-areas, packages will go to one of two camp zones 215. Insome embodiments, a worker or machine may scan a package (e.g., usingone of devices 119A-119C) to determine its eventual destination. Routingthe package to camp zone 215 may comprise, for example, determining aportion of a geographical area that the package is destined for (e.g.,based on a postal code) and determining a camp zone 215 associated withthe portion of the geographical area.

Camp zone 215, in some embodiments, may comprise one or more buildings,one or more physical spaces, or one or more areas, where packages arereceived from hub zone 213 for sorting into routes and/or sub-routes. Insome embodiments, camp zone 215 is physically separate from FC 200 whilein other embodiments camp zone 215 may form a part of FC 200.

Workers and/or machines in camp zone 215 may determine which routeand/or sub-route a package 220 should be associated with, for example,based on a comparison of the destination to an existing route and/orsub-route, a calculation of workload for each route and/or sub-route,the time of day, a shipping method, the cost to ship the package 220, aPDD associated with the items in package 220, or the like. In someembodiments, a worker or machine may scan a package (e.g., using one ofdevices 119A-119C) to determine its eventual destination. Once package220 is assigned to a particular route and/or sub-route, a worker and/ormachine may move package 220 to be shipped. In exemplary FIG. 2, campzone 215 includes a truck 222, a car 226, and delivery workers 224A and224B. In some embodiments, truck 222 may be driven by delivery worker224A, where delivery worker 224A is a full-time employee that deliverspackages for FC 200 and truck 222 is owned, leased, or operated by thesame company that owns, leases, or operates FC 200. In some embodiments,car 226 may be driven by delivery worker 224B, where delivery worker224B is a “flex” or occasional worker that is delivering on an as-neededbasis (e.g., seasonally). Car 226 may be owned, leased, or operated bydelivery worker 224B.

FIG. 3 is a flow chart illustrating an exemplary process fornormalization 300, consistent with the disclosed embodiments. At step302, external front end system 103 or internal front end system 105 mayrequest an address for normalization. The requested address may includea user's original address or may include any of multiple addressesassociated with a user's residential history. At step 304, externalfront end system 103 or internal front end system 105 may search a cachein order to determine whether or not a more refined version of therequested address exists or is available. For example, “123 Rolling ForkStreet” may be refined as “123 Rolling Fork St.” and “123 Rolling ForkSt.” may be currently stored in a cache. Address refinements may includeabbreviations or other types of corrections to address information thatare currently stored in cache. At step 306, external front end system103 or internal front end system 105 may run executable code todetermine whether cache is empty or not or whether the cache includes arefined address. At step 308, if a refined address already exists in thecache, meaning that cache “isNotEmpty,” then a refined address may bereturned as the normalized address to a user at a mobile device (e.g.,1196, 107A-107C in FIG. 1).

At step 310, external front end system 103 or internal front end system105 may also receive a request to normalize an address batch (instead ofa single address at step 302), including a plurality of addresses ormultiple pieces of address information. For example, the batch mayinclude a pattern for custom normalization address pattern 312representing a first address that may be similar to a pattern for customaddress normalization 314 representing a second address. As an example,“123 Rolling Fork Street” and “123 Rolling Fork Str.” may represent twosimilar patterns compared for normalization. At step 316, if any addressin the batch has failed to normalize by failing to conform to a refinedaddress stored in cache 304 or by failing to conform to a predeterminednormalization pattern, external front end system 103 or internal frontend system 105 may perform a normalization process at step 340 in orderto normalize the addresses and reduce redundancy. Accordingly, externalfront end system 103 or internal front end system 105 may run executablecode to generate a single address that may result as a normalizedaddress. As an example, at step 340, “123 Rolling Fork St.” mayrepresent the generated normalized address for “123 Rolling Fork Street”and “123 Rolling Fork Str.”

Returning to step 306, where an external front end system 103 orinternal front end system 105 may run an executable string of code todetermine whether cache “isNotEmpty,” at step 318, external front endsystem 103 or internal front end system 105 may determine that the cachedoes not include a refined address. External front end system 103 orinternal front end system 105 may then subsequently proceed to search adatabase in order to locate a refined address. Similarly, at step 320,external front end system 103 or internal front end system 105 maydetermine that the database does not include a refined address and maysubsequently proceed at step 322 to search a database normalizationengine in order to locate a refined address. Further, at step 324,external front end system 103 or internal front end system 105 maydetermine that the database normalization engine does not include arefined address and may proceed at step 330 to search a regularexpression (“regex”) pattern address normalization in order to locate arefined address at step 332. Executable strings of code including, forexample, “isNotEmpty” may be run by systems 103, 105 in order to searchthe above mentioned databases.

At step 326, external front end system 103 or internal front end system105 may locate a refined address and may publish the refined address byupdating external front end system 103 or internal front end system 105with the refined address in place of the requested address at step 302.After publishing the refined address, external front end system 103 orinternal front end system 105 may, at step 328, save a sub expressionassociated with the located refined address. At step 340, after saving,the refined address may be returned as the normalized address to a userat a mobile device (e.g., 119B, 107A-107C in FIG. 1).

Returning to step 330, after searching a regular expression (“regex”)pattern address normalization in order to locate a refined address,external front end system 103 or internal front end system 105 may notlocate a refined address at step 332. Subsequently, external front endsystem 103 or internal front end system 105 may publish at step 334 thatthere was a failure to normalize the address. Subsequently, at step 336,external front end system 103 or internal front end system 105 mayindicate as part of a notification or an alert that there was also afailure to normalize and save an associated sub expression. At step 338,Database Information Transmission Station (DITS) may store dataindicating that there was a failure to normalize an address and mayindicate at step 316 a failure to normalize the address information.This indication may include an alert or notification returned to a userat a mobile device (e.g., 119B, 107A-107C in FIG. 1).

FIG. 4 is a flow chart illustrating an exemplary process for machinelearning 400, consistent with the disclosed embodiments. At step 402,external front end system 103 or internal front end system 105 mayreceive an address from a user or customer. At step 404, an addressrefiner may receive the address from the user or customer as input andmay further refine the address (according to steps 304-340 as discussedin the above embodiment and shown in FIG. 3.) The address may becomprise personal, residential, or governmental data. At step 406, therefined address may be included as part of invoice information that maybe used at step 408 by a delivery worker as a correct address fordelivery or a package, consisted with the disclosed embodiments. Invoiceinformation may include any delivery information relating to a deliverypackage purchase including purchase price and also a valid deliveryaddress for shipment.

In some embodiments, a shipped address may be different from a refinedaddress that was shipped at step 408 and may be determined to be anincorrect address. External front end system 103 or internal front endsystem 105 (or a delivery worker) may submit the shipped address into amachine learning application at step 410. A machine learning applicationat step 410 may then compare a shipped address and a refined address inorder to determine a correct address. Subsequently, a machine learningapplication may apply at step 410 the determined correct address to theaddress refiner at step 404 for further address refining, processing ofinvoice information at step 406, and shipping of a refined address atstep 408. A machine learning application at step 404 may apply one ormore learning curves in order to correct address information asdiscussed in FIG. 5 below.

FIG. 5 is a diagrammatic illustration of an exemplary learning curve500, consistent with the disclosed embodiments. External front endsystem 103 or internal front end system 105 may determine commonmistakes that users make and develop patterns for automatic correctionof future addresses. External front end system 103 or internal front endsystem 105 may generate learning curves that represent a relationshipbetween correctness and information accumulation. For example, a hitratio 502 may be compared with accumulated data 504. For example, a hitratio 502 may be located on an X-axis, and accumulated addressinformation 504 may be located on a Y-axis. As discussed herein, a hitratio 502 means a ratio of inputted address accuracy to a storedpredetermined correct address. As a hit ratio 502 increases, so does anamount of accumulated data 504. Similarly, as an amount of accumulatedaddress data 504 increases, so does a hit ratio 502. As shown in FIG. 5,a learning curve 506 may appear linear in shape or may possess othercurvatures. It would be well understood by one of ordinary skill in theart that as address information is accumulated (504), an accuracy or hitratio 502 correctly describing an address would increase. External frontend system 103 or internal front end system 105 may extrapolate datafrom the curve 506 as shown in FIG. 5 to determine common mistakes thatusers make and develop patterns for automatic correction of futureaddresses. External front end system 103 or internal front end system105 may wait for a hit ratio 502 and accumulated data 504 to exceedpredetermined threshold values before developing patterns for automaticcorrection of future addresses. Other learning curves (not shown) may becontemplated.

FIG. 6 is a flow chart illustrating an exemplary process for collectionof address coordination information 600, consistent with the disclosedembodiments. At step 602, external front end system 103 or internalfront end system 105 may release a delivery parcel or package fordelivery. At step 604, external front end system 103 or internal frontend system 105 may refine a user or customer input address (consistentwith the embodiment of FIG. 3), and may save a refined address at step606 consistent with the above embodiments. As an example, as shown inFIG. 6, saved address information 606 may include latitude or longitudeinformation 608 for delivery and may be stored in database 610. Savedaddress information 606 may also include street number, street name,city, state, and zip code information.

As shown in FIG. 6, after refined address information is saved indatabase 610, external front end system 103 or internal front end system105 may determine that a delivery has commenced or started at apredetermined starting phase or starting time 612. Subsequently, at step614, external front end system 103 or internal front end system 105 maydetermine whether or not a delivery is complete. At step 616, externalfront end system 103 or internal front end system 105 may utilize GlobalPositioning System (GPS) or other location technologies in order toascertain a current location of a delivery parcel in transit, when it isdetermined that a delivery is not yet completed. At step 618, externalfront end system 103 or internal front end system 105 may compare thecurrent location of a delivery parcel in transit to a refined address'slocation. At step 620, external front end system 103 or internal frontend system 105 may determine whether or not the current location matcheswith the refined address's location. If there is no match, at step 622,external front end system 103 or internal front end system 105 may issuea warning of a wrong delivery location which may be transmitted to auser. However, if it is determined that the address does match, thedelivery will be determined as no longer released at step 624 and codedas arrived “A” or delivered at its proper delivery location. A warningof a wrong delivery location or indication of delivery arrival may becommunicated to a user at a mobile device (e.g., 119B, 107A-107C in FIG.1).

FIG. 7 is a flow chart illustrating an exemplary process for addressmatching 700, consistent with the disclosed embodiments. At step 702, auser or a customer may input an old address (or an outdated portion ofan address) 712 for use with external front end system 103 or internalfront end system 105. For example, as shown in FIG. 7, an address may be“123 Rolling Fork St., Apt. 730, Alexandria, Va. 22314.” The old oroutdated portion of the address may be “St.” and “Apt. 730.”

At step 704, external front end system 103 or internal front end system105 may divide the customer address into component parts 714, includingfor example, street number, street name, apartment number, city, state,and zip code. As shown in FIG. 7, this may include “123,” “RollingFork,” “Rolling Fork St., Apt. 730,” “730,” “Alexandria,” “VA,” or“22314.” Other divisions or component parts not shown may becontemplated.

At step 706, external front end system 103 or internal front end system105 may filter the address using an outdated or old address dictionary720. As shown in FIG. 7, old address dictionary 720 may include city,street number, full street portions. Old address dictionary 720 may alsoinclude other address information portions (not shown) used forfiltering. External front end system 103 or internal front end system105 may filter address information into two portions 716 including a“regular address” that includes street information and a “customer'sadditional address” that may include city, state, and zip codeinformation.

At step 708, external front end system 103 or internal front end system105 may match the new address by a new address dictionary 722. Forexample, “123” may be matched with “123,” “Rolling Fork” may be matchedwith “Rolling Fork,” and “Rolling Fork St. Apt. 730” may be matched with“Rolling Fork Rd. Apt. 370.” External front end system 103 or internalfront end system 105 may determine, based on the matching, that someportions of old address information need to be updated or automaticallycorrected. Accordingly, at step 710, external front end system 103 orinternal front end system 105 may change or replace an old address to anew corrected address 718 such that the new address reads “123 RollingFork Rd., Apt. 370, Alexandria, Va. 22314.”

FIG. 8 is a flow chart illustrating an exemplary process for automaticaddress correction, consistent with the disclosed embodiments. While theexemplary method 800 is described herein as a series of steps, it is tobe understood that the order of the steps may vary in otherimplementations. In particular, steps may be performed in any order, orin parallel.

At step 802, external front end system 103 or internal front end system105 may receive, from a user device, a first user input including firstaddress information. As shown in FIG. 7, first address information 712may include a street number, street name, apartment number, city, state,and zip code. Consistent with disclosed embodiments, first addressinformation 712 may include, as an example. “123 Rolling Fork St., Apt.730, Alexandria, Va. 22314.” External front end system 103 or internalfront end system 105 may be implemented as a server and may receive thefirst address information 712 inputted manually by a user at a userinterface or automatically from at least one of a web browser on acomputer system (e.g., 119A or 119C in FIG. 1), a mobile device (e.g.,119B, 107A-107C in FIG. 1), a database, or the like.

At step 804, external front end system 103 or internal front end system105 may compare (as also shown in step 618 of FIG. 6), based on thefirst user input, the first address information 712 against secondaddress information 718, wherein the second address information 718 maybe stored in a database. As shown in FIGS. 3, 4, and 6, comparingaddress information may include normalizing address information orrefining address information. For example, custom normalize pattern 312of a first address may be the same as custom normalize pattern 314 of asecond address. At step 316, if an address has failed to normalize overthe course of multiple analogous iterations including “123 Rolling ForkStreet” and “123 Rolling Fork Str.,” a normalization process at step 340may take place as shown in FIG. 3 in order to normalize the address andreduce redundancy. Accordingly, a single address may result as anormalized address. As an example, “123 Rolling Fork St.” may representthe normalized address for “123 Rolling Fork Street” and “123 RollingFork Str.”

Comparing address information may also include refining addressinformation. As shown in FIG. 4, at step 404, external front end system103 or internal front end system 105 may include an address refinerconfigured to receive address 402 from the user or customer as input andmay further refine the address (as also discussed in steps 304-340 aboveand shown in FIG. 3.) For example, at step 304, external front endsystem 103 or internal front end system 105 may search cache in order todetermine whether or not a more refined version of the requested addressexists or is available. For example, “123 Rolling Fork Street” may berefined as “123 Rolling Fork St.” Address refinements may includeabbreviations or other corrections to a request address. At step 306,external front end system 103 or internal front end system 105 may runan executable string of code to determine whether cache “isNotEmpty.” Atstep 308, if a refined address already exists in cache meaning thatcache ““isNotEmpty,” then a refined address may be returned to a user ata mobile device (e.g., 119B, 107A-107C in FIG. 1).

As shown in FIG. 7, second address information 718 may also include astreet number, street name, apartment number, city, state, and zip code.Second address information 718 may also include at least one of storedlocation data, stored map data, known address region or neighborhooddivisions, and a residence title. The second address information mayhave been originally been entered in external front end system 103 orinternal front end system 105 by a user (e.g., a delivery workers, acustomer, or a customer support worker) in any of a web browser on acomputer system (e.g., 119A or 119C in FIG. 1), a mobile device (e.g.,119B, 107A-107C in FIG. 1), a database, or the like. As shown in FIG. 7,first address information 712 may be compared to second addressinformation 718 residing in old address dictionary 720 and new addressdictionary 722. Other methods for comparison may be contemplated,consistent with disclosed embodiments.

At step 806, external front end system 103 or internal front end system105 may determine, based on the comparison, whether the first addressinformation matches the second address information (see also step 618).External front end system 103 or internal front end system 105 maycommunicate, based on the determination that the first addressinformation matches the second address information, a notification tothe user that there are differences/a wrong location (FIG. 6, step 622)or no differences between the first address information and the secondaddress information. The notification may include any of an alert ormessage that may be sent to delivery workers, customers, or customersupport. The notification may be displayed on a graphical user interface(GUI) and may allow for the user to indicate receipt of the messageindicating that the user is aware that there are no differences betweenthe first address information and the second address information.Differences may include misspellings, non-standardization of addressinformation, typographical errors, differences in numerals, differencesin spacing, differences in capitalization, or any other abnormaldifferences detected between first address information and secondaddress information.

As an example, as shown in FIG. 6 at step 620, external front end system103 or internal front end system 105 may determine whether or not thecurrent location matches with the refined address's location. If thereis no match, at step 622, a warning of a wrong delivery location may betransmitted to a user. However, if it is determined that the addressesmatch, the delivery will be determined as no longer released at step 624and coded as arrived “A” or delivered at its proper delivery location.

As another example, as shown in FIG. 7 at step 708, external front endsystem 103 or internal front end system 105 may match the new address bya new address dictionary. For example, “123” may be matched with “123,”“Rolling Fork” may be matched with “Rolling Fork,” and “Rolling Fork St.Apt. 730” may be matched with “Rolling Fork Rd. Aprt. 370.” Finally, atstep 710, external front end system 103 or internal front end system 105may change or replace an old address to a new address such that the newaddress reads “123 Rolling Fork Rd., Apt. 370, Alexandria, Va. 22314.”Other methods for determining whether address information matches may becontemplated, consistent with disclosed embodiments.

At step 808, external front end system 103 or internal front end system105 may compute, based on a determination that the first addressinformation does not match the second address information (as shown inFIGS. 6 and 7, and discussed above at steps 622 and 708), firstdifferences between the first address information and the second addressinformation. The matching may be determined as part of a comparisonbetween the first address information and the second addressinformation. External front end system 103 or internal front end system105 may communicate, based on the first differences exceeding apredetermined threshold (as shown in FIG. 6 and discussed above at step618), a notification 622 to the user that the first address informationdoes not match stored address information in the database. Thepredetermined threshold may include a predetermined number ofdifferences from the second address information, and wherein thedifferences include at least one of differences in numbers, streetnames, postal codes, city names, and district names. The predeterminedthreshold may also include only a single difference or the absence ofexact matching of address information. The predetermined threshold mayalso be configured for comparison of only one or more of identifiedmisspellings, non-standardization of address information, typographicalerrors, differences in numerals, differences in spacing, differences incapitalization, or any other differences between first addressinformation and second address information. Multiple predeterminedthresholds may be contemplated.

As an example, at step 704, external front end system 103 or internalfront end system 105 may divide the customer address into componentparts, including for example, street number, street name, apartmentnumber, city, state, and zip code. As shown in FIG. 7, this may include“123,” “Rolling Fork,” “Rolling Fork St., Apt. 730,” “730,”“Alexandria,” “VA,” or “22314.” Component part “123” may be matched with“123,” “Rolling Fork” may be matched with “Rolling Fork,” and “RollingFork St. Apt. 730” may be matched with “Rolling Fork Rd. Apt. 370” inorder compute the differences in apartment number (e.g. 730 vs. 370) andstreet name (e.g. St. vs. Rd.) between the two addresses.

In some embodiments, external front end system 103 or internal front endsystem 105 may store corrected address information in addition to firstand second address information. For example, external front end system103 or internal front end system 105 may store corrected third addressinformation and corrected fourth address information. External front endsystem 103 or internal front end system 105 may also store this addressinformation in a database, and determine, based on multiple differences(including the identified first differences, second differences, thirddifferences, or additional differences), common differences that existfor multiple user inputs (including, but not limited to, first, second,and third user inputs) corresponding to input of address information.External front end system 103 or internal front end system 105 may alsodevelop, based on the determination of common differences, a correctivepattern to correct future user input that includes address information.The server may generate, by the processor, a learning curve 506 (asshown in FIG. 5 as discussed above) based on any or all of the correctedaddress information, develop, based on the determination of commondifferences, one or more corrective patterns, and modify, based on thegeneration of a learning curve 506 and the corrective patterns, thepredetermined threshold for correcting inputted address information (seeFIG. 7 as discussed above).

As an example, one or more corrective patterns may include normalizationor refinement patterns for address information, consistent with theexemplary processes of FIGS. 3 and 4. In addition to one or morecorrective patterns, one or more learning curves 506 may be generated inorder to develop the one or more corrective patterns for automaticaddress correction of a future address input. External front end system103 or internal front end system 105 may determine common mistakes thatusers make and develop patterns for automatic correction of futureaddresses. As shown in FIG. 4, corrective patterns may be inputted intomachine learning application at step 410. Machine learning applicationat step 410 may then compare corrective patterns to received addressinformation in order to provide for automatic address correction of afuture address input.

At step 810, external front end system 103 or internal front end system105 operating as a server may automatically correct, based ondifferences not exceeding a predetermined threshold, first addressinformation to match second address information. As an example, as shownin FIG. 7 at step 710, external front end system 103 or internal frontend system 105 may automatically change or replace an old address to anew address such that the new address reads “123 Rolling Fork Rd., Apt.370, Alexandria, Va. 22314” instead of “123 Rolling Fork St., Apt. 730,Alexandria, Va. 22314”

In some aspects, external front end system 103 or internal front endsystem 105 may receive, from the user, a second user input includingthird address information; compare, based on the second user input, thethird address information against the second address information; anddetermine, based on the comparison, whether the third addressinformation matches the second address information. In other aspects,external front end system 103 or internal front end system 105 mayreceive, from the user, a new user input including new addressinformation (e.g. third, fourth, or fifth address information); compare,based on the new user input, the new address information against storedaddress information; and determine, based on the comparison of the newaddress information to the stored information, whether the new addressinformation matches the stored address information, consistent withdisclosed embodiments. In further aspects, external front end system 103or internal front end system 105 may compute, based on a determinationthat additional address information (e.g. different from newly enteredaddress information) does not match the stored address information,entirely new differences between the additional address information andthe stored address information, and correct, based on these differencesnot exceeding the predetermined threshold, the additional addressinformation to match the stored address information, consistent withdisclosed embodiments.

In other aspects, external front end system 103 or internal front endsystem 105 may compute, based on the determination that new addressinformation does not match the stored address information, differencesbetween the new address information and the stored address information,and may correct, based on the differences not exceeding anypredetermined thresholds, the new address information to match thestored address information. In some aspects, external front end system103 or internal front end system 105 may allow for searching for theaddress information provided by the user at step 802, and returning,based on the searched first address information, the corrected addressinformation. In some embodiments, inputting a search query at step 802may trigger a system (e.g., external front end system 103 or internalfront end system 105) to perform steps 804-810 and to correct receivedaddress information. In some embodiments, exemplary process 800 may sendcorrected address information to one or more devices (e.g., the devicesending the input in step 802). For example, in a situation where acustomer device (e.g., 102B in FIG. 1) sends an address as part ofcompleting an online shopping order, external front end system 103 mayrespond by sending corrected address information to customer device 102Bto request confirmation of the corrected address information as well asto other devices (e.g., SAT system 101 or seller portal 109) in order toinitiate ordering of the item.

While the present disclosure has been shown and described with referenceto particular embodiments thereof, it will be understood that thepresent disclosure can be practiced, without modification, in otherenvironments. The foregoing description has been presented for purposesof illustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations will beapparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments. Additionally,although aspects of the disclosed embodiments are described as beingstored in memory, one skilled in the art will appreciate that theseaspects can also be stored on other types of computer readable media,such as secondary storage devices, for example, hard disks or CD ROM, orother forms of RAM or ROM, USB media, DVD, Blu-ray, or other opticaldrive media.

Computer programs based on the written description and disclosed methodsare within the skill of an experienced developer. Various programs orprogram modules can be created using any of the techniques known to oneskilled in the art or can be designed in connection with existingsoftware. For example, program sections or program modules can bedesigned in or by means of .Net Framework, .Net Compact Framework (andrelated languages, such as Visual Basic, C, etc.), Java, C++,Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with includedJava applets.

Moreover, while illustrative embodiments have been described herein, thescope of any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose skilled in the art based on the present disclosure. Thelimitations in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application.The examples are to be construed as non-exclusive. Furthermore, thesteps of the disclosed methods may be modified in any manner, includingby reordering steps and/or inserting or deleting steps. It is intended,therefore, that the specification and examples be considered asillustrative only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

What is claimed is:
 1. A computer-implemented system for correctingaddress information, the system comprising: a memory storinginstructions; and at least one processor configured to execute theinstructions to: receive, from a user device, an address fornormalization, wherein the address is one of a current address or ahistorical residential address of a user; normalize the address; comparea location associated with the normalized address to a current locationof a delivery worker; based on a determination that the normalizedaddress location does not match the current location of the deliveryworker, transmit a warning to the user device; correct the normalizedaddress, based on a machine learning process; store, based on thecorrection, the corrected normalized address in a database; provideinstructions to a mobile device of the delivery worker to deliver apackage to the user at a delivery location based on the correctednormalized address; generate, by the processor, a learning curve basedon the corrected normalized address information; develop, based on adetermination of common differences, a corrective pattern; and modify,based on the generation of a learning curve and the corrective pattern,a predetermined threshold for correcting inputted address information.2. The computer-implemented system of claim 1, wherein the determinationthat the normalized address location does not match the current locationof the delivery worker comprises determining that an amount of detecteddifferences between the normalized address location and the currentlocation of the delivery worker exceeds a predetermined threshold. 3.The computer-implemented system of claim 2, wherein the predeterminedthreshold includes a predetermined number of differences between thenormalized address location and the current location of the deliveryworker, and wherein the differences include at least one of differencesin numbers, street names, zip codes, city names, and district names. 4.The computer-implemented system of claim 1, wherein the location of thedelivery worker includes at least one of stored location data, storedmap data, region, neighborhood divisions, or a residence title.
 5. Thecomputer-implemented system of claim 1, wherein the at least oneprocessor is further configured to execute the instructions to: compare,while the package is in transit, the normalized address location toaddress information stored in a database, wherein the database includesa dictionary of address terms.
 6. The computer-implemented system ofclaim 1, wherein the at least one processor is further configured toexecute the instructions to: develop, based on a determination ofmultiple differences between the normalized address and the currentlocation of the delivery worker, a corrective pattern to correct futureuser input that includes address information.
 7. Thecomputer-implemented system of claim 1, wherein the at least oneprocessor is further configured to execute the instructions to: receivethe user input including requested address information from the userfrom at least one of a web browser, a mobile device, or a user database.8. A computer-implemented method for correcting address information, themethod comprising: receiving, from a user device, an address fornormalization, wherein the address is one of a current address or ahistorical residential address of a user; normalizing the address;comparing a location associated with the normalized address to a currentlocation of a delivery worker; based on a determination that thenormalized address location does not match the current location of thedelivery worker, transmitting a warning to the user device; correctingthe normalized address, based on a machine learning process; storing,based on the correction, the corrected normalized address in a database;providing instructions to a mobile device of the delivery worker todeliver a package to the user at a delivery location based on thecorrected normalized address; generating, by the processor, a learningcurve based on the corrected normalized address information; developing,based on a determination of common differences, a corrective pattern;and modifying, based on the generation of a learning curve and thecorrective pattern, a predetermined threshold for correcting inputtedaddress information.
 9. The computer-implemented method of claim 8, themethod comprising: communicating, if it is determined that an amount ofdetected differences between the normalized address location and thecurrent location of the delivery worker exceeds a predeterminedthreshold, a notification to the user that the normalized addresslocation does not match the current location of the delivery worker. 10.The computer-implemented method of claim 8, the method comprising:receiving the user input including requested address information fromthe user from at least one of a web browser, a mobile device, or a userdatabase.
 11. The computer-implemented method of claim 8, wherein thelocation of the delivery worker includes at least one of stored locationdata, stored map data, region, neighborhood divisions, or a residencetitle.
 12. The computer-implemented method of claim 8, the methodcomprising: comparing, while the package is in transit, the normalizedaddress location to address information stored in a database, whereinthe database includes a dictionary of address terms.
 13. Thecomputer-implemented method of claim 8, wherein the at least oneprocessor is further configured to execute the instructions to: develop,based on a determination of multiple differences between the normalizedaddress and the current location of the delivery worker, a correctivepattern to correct future user input that includes address information.14. The computer-implemented method of claim 8, wherein thepredetermined threshold includes a predetermined number of differencesbetween the normalized address location and the current location of thedelivery worker, and wherein the differences include at least one ofdifferences in numbers, street names, zip codes, city names, anddistrict names.
 15. A non-transitory computer readable medium comprisingexecutable instructions that when executed by at least one processingdevice cause the at least one processing device to correct addressinformation and perform operations comprising: receiving, from a userdevice, an address for normalization, wherein the address is one of acurrent address or a historical residential address of a user;normalizing the address; comparing a location associated with thenormalized address to a current location of a delivery worker; based ona determination that the normalized address location does not match thecurrent location of the delivery worker, transmitting a warning to theuser device; correcting the normalized address, based on a machinelearning process; storing, based on the correction, the correctednormalized address in a database; providing instructions to a mobiledevice of the delivery worker to deliver a package to the user at adelivery location based on the corrected normalized address; generating,by the processor, a learning curve based on the corrected normalizedaddress information; developing, based on a determination of commondifferences, a corrective pattern; and modifying, based on thegeneration of a learning curve and the corrective pattern, apredetermined threshold for correcting inputted address information. 16.The non-transitory computer readable medium of claim 15, wherein thedetermination that the normalized address location does not match thecurrent location of the delivery worker comprises: determining that anamount of detected differences between the normalized address locationand the current location of the delivery worker exceeds a predeterminedthreshold.
 17. The non-transitory computer readable medium of claim 15,wherein the operations further comprise: receiving the user inputincluding requested address information from the user from at least oneof a web browser, a mobile device, or a user database.